An Improved Algorithm for Network Intrusion Detection Based on Deep Residual Networks

被引：1

作者：

Hu, Xuntao ^{[1
]}

Meng, Xiancai ^{[2
]}

Liu, Shaoqing ^{[2
]}

Liang, Lizhen ^{[2
]}

机构：

[1] Anhui Univ Sci & Technol, Huainan 232001, Peoples R China

[2] Hefei Comprehens Natl Sci Ctr, Inst Energy, Hefei 230031, Anhui, Peoples R China

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Logic gates; Feature extraction; Intrusion detection; Adaptation models; Convolutional neural networks; Residual neural networks; Data models; Long short term memory; Bidirectional control; residual networks; hybrid attention mechanisms; bidirectional long and short memory networks; AUTHORIZATION USAGE CONTROL; SAFETY DECIDABILITY; INTERNET; THINGS;

D O I：

10.1109/ACCESS.2024.3398007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The goal of current research will be to increase the accuracy and generalisation capacity of intrusion detection models in order to better handle the complex network security issues of today. In this paper, a new hybrid attention mechanism is introduced along with an enhanced algorithm. Through the effective channel layer and curve space layer, the feature information will be concentrated on the necessary feature information, allowing the model to concentrate more on the features linked to classification and become more broadly applicable. Increase the model's precision. The experimental results demonstrated that the accuracy can achieve 100%, 99.79%, and 98.10% on binary classification problems and 96.37%, 98.12%, and 99.06% on multiclassification problems, respectively, using the UNSW-NB15, CICIDS-2017, and CICIDS-2018 datasets for validation.

引用

页码：66432 / 66441

页数：10

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